Adaptive recommendations systems

Abstract

An adaptive recommendation system and a mobile adaptive recommendation system are disclosed. The adaptive recommendation system and the mobile adaptive recommendation system include algorithms for monitoring user usage behaviors across a plurality of usage behavior categories associated with a computer-based system, and generating recommendations based on inferences on user preferences and interests. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.

What is claimed is: 
     
       1. An adaptive recommendation system, comprising:
 at least one storage device configured to store a plurality of aspects comprising:  a content aspect comprising information;   a computer-implemented structural aspect comprising the content aspect and associated relationships; and    a usage aspect, comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users of the system; and   
 at least one processing device configured to execute a plurality of functions comprising: 
 a function to generate a user tunable adaptive recommendation based, at least in part, on a user's navigational context and on an automatic inference of the user's interests from a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories; and 
 a function to deliver the adaptive recommendation to the user one or more users. 
 
 
     
     
       2. The adaptive recommendation system of  claim 1 , wherein the information is selected from a group consisting of text, graphics, audio, video, interactive forms of content, applets, tutorials, advertising content, courseware, demonstrations, representations of people, modules, executable code, and computer programs. 
     
     
       3. The adaptive recommendation system of  claim 1 , wherein the computer-implemented structural aspect further comprises:
 one or more objects, each object comprising the information; and 
 one or more relationships, wherein each relationship is associated with each pair of the one or more objects. 
 
     
     
       4. The adaptive recommendation system of  claim 1 , the usage aspect further comprising one or more usage behaviors, wherein each usage behavior is associated with either a user, one or more user communities, or a the user and the one or more user communities simultaneously, wherein the user comprises a single-member subset of the one or more users and a community of the one or more user communities comprises a multiple-member subset of the one or more users. 
     
     
       5. The adaptive recommendation system of  claim 1 , wherein a user of the one or more users is selected from a group consisting of a computer-based system, a second adaptive system, and a human being. 
     
     
       6. The adaptive recommendation system of  claim 1 , the plurality of usage behaviors further comprising private behaviors and non-private behaviors. 
     
     
       7. The adaptive recommendation system of  claim 1 , further comprising a privacy control, the privacy control enabling a user of the one or more users to restrict usage behaviors associated with the user from being deemed non-private behaviors. 
     
     
       8. The adaptive recommendation system of  claim 1 , wherein a the function to generate a the user tunable adaptive recommendation based, at least in part, on a user's the navigational context of the one or more users and on an the automatic inference of the user's interests of the one or more users from a the plurality of usage behaviors associated with the one or more users corresponding to a the plurality of usage behavior categories further comprises:
 usage behavior categories, wherein the usage behavior categories are selected from a group consisting of navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, and physical location patterns. 
 
     
     
       9. The adaptive recommendation system of  claim 1 , wherein a the function to generate a the user tunable adaptive recommendation based, at least in part, on a user's the navigational context of the one or more users and on an the automatic inference of the user's interests of the one or more users from a the plurality of usage behaviors associated with the one or more users corresponding to a the plurality of usage behavior categories further comprises:
 a function that infers user preferences from the plurality of usage behaviors. 
 
     
     
       10. The adaptive recommendation system of  claim 9 , wherein a the function that infers user preferences from the plurality of usage behaviors further comprises:
 an algorithm that prioritizes application of usage patterns associated with a the plurality of usage behavior categories. 
 
     
     
       11. The adaptive recommendation system of  claim 9 , wherein a the function that infers user preferences the algorithm from the plurality of usage behaviors comprises a statistical learning algorithm, wherein the statistical learning algorithm is selected from a group consisting of Bayesian modeling, neural network modeling, k-nearest neighbor modeling, and support vector machine modeling. 
     
     
       12. The adaptive recommendation system of  claim 1 , wherein a the function to generate a the user tunable adaptive recommendation based, at least in part, on a user's the navigational context of the one or more users and on an the automatic inference of the user's interests of the one or more users from a the plurality of usage behaviors associated with the one or more users corresponding to a the plurality of usage behavior categories further comprises:
 an algorithm that detects apparent insincere system usage behaviors or other inferred “gaming” gaming behaviors by the one or more users. 
 
     
     
       13. The adaptive recommendation system of  claim 1 , wherein a the function to generate a the user tunable adaptive recommendation based, at least in part, on a user's the navigational context of the one or more users and on an the automatic inference of the user's interests of the one or more users from a the plurality of usage behaviors associated with the one or more users corresponding to a the plurality of usage behavior categories further comprises:
 a compensatory algorithm associated with the detection of apparent insincere system usage behaviors or other inferred “gaming” gaming behaviors by the one or more users. 
 
     
     
       14. The adaptive recommendation system of  claim 1 , wherein a the function to generate a the user tunable adaptive recommendation based, at least in part, on a user's the navigational context of the one or more users and on an the automatic inference of the user's interests of the one or more users from a the plurality of usage behaviors associated with the one or more users corresponding to a the plurality of usage behavior categories further comprises:
 an algorithm that applies pattern matching of information embodied in the structural aspect and content aspect to produce content interpretation patterns, and associates the content interpretation patterns with usage patterns. 
 
     
     
       15. The adaptive recommendation system of  claim 1 , wherein the user tunable adaptive recommendation further comprises:
 a structural subset of the structural aspect, the structural subset comprising at least one of the one or more objects and associated relationships of the one or more objects of the structural aspect. 
 
     
     
       16. The adaptive recommendation system of  claim 1 , wherein a the function to deliver the adaptive recommendation to the user one or more users comprises:
 a recommendation delivery mode, wherein the recommendation delivery mode is selected from a group consisting of visual, audio, and a combination of visual and audio. 
 
     
     
       17. The adaptive recommendation system of  claim 1 , wherein a the function to deliver the adaptive recommendation to the user one or more users comprises:
 a recommendation delivery means, wherein the recommendation delivery means is selected from a group consisting of user in-context system usage, direct user requests, and out-of-the-context of system usage. 
 
     
     
       18. A mobile adaptive recommendation system, comprising:
 at least one storage device configured to store a plurality of aspects comprising:  a content aspect comprising information;   a computer-implemented structural aspect comprising the content aspect and associated relationships; and    a usage aspect, comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users; and   
 at least one processing device configured to execute a plurality of functions comprising: 
 a function to automatically determine the location of a user based on physical location data generated by a location-aware device; 
 a user-controlled recommendation tuning function; 
 a function to generate an adaptive recommendation based, at least in part, on the user's recommendation tuning settings and on the automatically determined location of the user and at least one other of the usage behavior behaviors associated with the one or more users corresponding to at least one other usage behavior category; and 
 
 a function to deliver the adaptive recommendation to the user. 
 
     
     
       19. The mobile adaptive recommendation system of  claim 18 , wherein a the function to generate an the adaptive recommendation based, at least in part, on the user's recommendation tuning settings and on the automatically determined location of the user and at least one other usage behavior associated with the one or more users corresponding to at least one other usage behavior category further comprises:
 an algorithm to determine the change in location of a the user as a function of time. 
 
     
     
       20. An article comprising a physical non-transitory computer-readable medium storing instructions for enabling a processor-based system to:
 access a content aspect comprising information; 
 access a structural aspect comprising the content aspect and associated relationships; 
 access a usage aspect, comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users; 
 generate an user tunable adaptive recommendation based, at least in part, on a user's navigational context and on an automatic inference of the user's interests from a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories; and 
 deliver the adaptive recommendation to the user. 
 
     
     
       21. An adaptive recommendation system, comprising:
 at least one storage device configured to store a plurality of aspects comprising:
 a content aspect comprising information; 
 a structural aspect comprising the content aspect and associated relationships; and 
 a usage aspect comprising captured usage behaviors associated with users of the adaptive recommendation system and corresponding to a plurality of usage behavior categories; and 
   at least one processing device configured to execute:
 a function to generate user tunable adaptive recommendations based, at least in part, on a navigational context of the users and on an automatic inference of interests of the users from a plurality of the captured usage behaviors associated with the users and corresponding to the plurality of usage behavior categories; and 
 a function to deliver the user tunable adaptive recommendations to at least one of the users.  
   
     
     
       22. The adaptive recommendation system of claim 21, wherein the information includes text, graphics, audio, video, interactive forms of content, applets, tutorials, advertising content, courseware, demonstrations, representations of people, modules, executable code, or computer programs.  
     
     
       23. The adaptive recommendation system of claim 21,
 wherein the structural aspect further includes objects with at least a portion of the information; and   wherein each of the associated relationships is configured to associate a pair of the objects.    
     
     
       24. The adaptive recommendation system of claim 21,
 wherein the captured usage behaviors are associated with either the users or one or more user communities or the users and the one or more user communities;   wherein each of the users comprises a single-member subset of the users; and   wherein a community of the one or more user communities comprises a multiple-member subset of the users.    
     
     
       25. The adaptive recommendation system of claim 21, wherein the users are selected from a group consisting of a computer-based system, a second adaptive system, and a human being.  
     
     
       26. The adaptive recommendation system of claim 21, wherein the captured usage behaviors further comprise private behaviors and non-private behaviors.  
     
     
       27. The adaptive recommendation system of claim 21, further comprising a privacy control configured to enable the users to restrict the captured usage behaviors from being deemed non-private behaviors.  
     
     
       28. The adaptive recommendation system of claim 21, wherein the plurality of usage behavior categories includes navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, or physical location patterns.  
     
     
       29. The adaptive recommendation system of claim 21, wherein the function to generate the user tunable adaptive recommendations includes a function configured to infer preferences of the users from the captured usage behaviors.  
     
     
       30. The adaptive recommendation system of claim 29, wherein the function configured to infer preferences of the users from the captured usage behaviors includes an algorithm configured to prioritize application of usage patterns associated with the plurality of usage behavior categories.  
     
     
       31. The adaptive recommendation system of claim 29, wherein the function configured to infer preferences of the users from the captured usage behaviors includes a statistical learning algorithm selected from a group consisting of Bayesian modeling, neural network modeling, k-nearest neighbor modeling, and support vector machine modeling.  
     
     
       32. The adaptive recommendation system of claim 21, wherein the function to generate user tunable adaptive recommendations includes an algorithm configured to detect apparent insincere system usage behaviors or other inferred gaming behaviors by the users.  
     
     
       33. The adaptive recommendation system of claim 21, wherein the function to generate user tunable adaptive recommendations includes a compensatory algorithm configured to detect apparent insincere system usage behaviors or other inferred gaming behaviors by the users.  
     
     
       34. The adaptive recommendation system of claim 21, wherein the function to generate user tunable adaptive recommendations includes an algorithm configured to apply pattern matching of the information embodied in the structural aspect and the content aspect to produce content interpretation patterns and configured to associate the content interpretation patterns with usage patterns.  
     
     
       35. The adaptive recommendation system of claim 21, wherein a structural subset of the structural aspect includes at least one object and wherein the relationships are associated with the least one object.  
     
     
       36. The adaptive recommendation system of claim 21,
 wherein the function to deliver the user tunable adaptive recommendations includes a recommendation delivery mode; and   wherein the recommendation delivery mode is selected from a group consisting of visual, audio, and a combination of visual and audio.    
     
     
       37. The adaptive recommendation system of claim 21, wherein the function to deliver the user tunable adaptive recommendations includes a delivery means comprising in-context system usage by the users, direct requests by the users, or out-of-context system usage.  
     
     
       38. A mobile adaptive recommendation system, comprising:
 at least one storage device configured to store a plurality of aspects comprising:
 a content aspect comprising information; 
 a structural aspect comprising the content aspect and associated relationships; and 
 a usage aspect comprising captured usage behaviors associated with a user; and 
   at least one processing device configured to execute:
 a function to automatically determine a location of the user based on physical location data generated by a location-aware device; 
 a user-controlled recommendation tuning function; 
 a function to generate an adaptive recommendation based, at least in part, on recommendation tuning settings, the determined location of the user, and at least one of the captured usage behaviors associated with the user and corresponding to at least one usage behavior category; and 
 a function to deliver the adaptive recommendation to the user.  
   
     
     
       39. The mobile adaptive recommendation system of claim 38, wherein the function to generate the adaptive recommendation includes an algorithm to determine a change in the location of the user as a function of time.  
     
     
       40. An article comprising a non-transitory computer-readable medium storing instructions that, in response to execution by a processing device, cause the processing device to perform operations comprising:
 access a content aspect comprising information;   access a structural aspect comprising the content aspect and associated relationships;   access a usage aspect comprising captured usage behaviors associated with a user;   generate a user tunable adaptive recommendation based, at least in part, on a navigational context of the user and on an automatic inference of interests of the user from the captured usage behaviors associated with the user and corresponding to usage behavior categories; and   deliver the user tunable adaptive recommendation to the user.